Tracing with Stackdriver on Kubernetes Engine
When supporting a production system that services HTTP requests or provides an API, it is important to measure the latency of your endpoints to detect when a system's performance is not operating within specification. In monolithic systems this single latency measure may be useful to detect and diagnose deteriorating behavior. With modern microservice architectures, however, this becomes much more difficult because a single request may result in numerous additional requests to other systems before the request can be fully handled. Deteriorating performance in an underlying system may impact all other systems that rely on it. While latency can be measured at each service endpoint, it can be difficult to correlate slow behavior in the public endpoint with a particular sub-service that is misbehaving.
Enter distributed tracing. Distributed tracing uses metadata passed along with requests to correlate requests across service tiers. By collecting telemetry data from all the services in a microservice architecture and propagating a trace id from an initial request to all subsidiary requests, developers can much more easily identify which service is causing slowdowns affecting the rest of the system.
Google Cloud Platform (GCP) provides the Stackdriver suite of products to handle logging, monitoring, and distributed tracing. This lab will be deployed to Kubernetes Engine and will demonstrate a multi-tier architecture implementing distributed tracing. It will also take advantage of Terraform to build out necessary infrastructure.
This lab was created by GKE Helmsman engineers to give you a better understanding of GKE Binary Authorization. You can view this demo on Github here. We encourage any and all to contribute to our assets!
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Use Terraform to set up the necessary infrastructure
Create Stackdriver workspace
Deploy demo application
Generate Telemetry Data